> ## Documentation Index > Fetch the complete documentation index at: https://docs.fieldfunded.com/llms.txt > Use this file to discover all available pages before exploring further. # Web Scraping Sports Odds vs Using an API > Should you scrape sports odds or use an API? Compare reliability, cost, legal risk, and speed. Complete code examples for both approaches. # Web Scraping Sports Odds vs Using an API If you need sports odds data, you have two options: scrape betting sites or use a dedicated API. This article compares both with real code, real numbers, and real trade-offs — so you can pick the right approach for your project. **Short answer**: Scraping works for one-off research. For anything production-grade — bots, apps, dashboards — an API saves you weeks of maintenance. ## Side-by-Side Comparison | Factor | Web Scraping | Odds API | | --------------- | --------------------------------- | --------------------------- | | Setup time | 2-8 hours | 5 minutes | | Maintenance | Weekly fixes (selectors break) | Zero | | Data format | Raw HTML → parse yourself | Structured JSON | | Coverage | 1 site at a time | 30+ sports, 1,000+ markets | | Reliability | \~70% uptime (CAPTCHAs, bans) | 99.9% uptime SLA | | Legal risk | TOS violations, possible lawsuits | Licensed, legal | | Latency | 2-10 seconds per page | \<300ms per request | | Cost | Free (but your time) | Free tier available | | Settlement data | Not available | Automatic (won/lost/refund) | ## The Scraping Approach Here is what a basic odds scraper looks like with Python and BeautifulSoup: ```python theme={null} # odds_scraper.py — Scrape odds from a public site # WARNING: This may violate the site's TOS import requests from bs4 import BeautifulSoup url = "https://example-odds-site.com/soccer/premier-league" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.text, "html.parser") # These selectors WILL break when the site updates matches = soup.select(".match-row") for match in matches: home = match.select_one(".team-home").text.strip() away = match.select_one(".team-away").text.strip() odds_1 = match.select_one(".odds-home").text.strip() odds_x = match.select_one(".odds-draw").text.strip() odds_2 = match.select_one(".odds-away").text.strip() print(f"{home} vs {away}: {odds_1} / {odds_x} / {odds_2}") ``` ### Problems You Will Hit 1. **Selectors break**: The site redesigns, and `.match-row` becomes `.event-card`. Your scraper is down until you fix it. 2. **JavaScript rendering**: Many odds sites use React/Angular. `requests.get()` returns an empty page. You need Selenium or Playwright, which are 10x slower. 3. **CAPTCHAs and rate limits**: After 50-100 requests, you get blocked. Now you need proxies (\$20-50/month). 4. **IP bans**: Rotate proxies, solve CAPTCHAs, handle cookies — your 20-line script becomes 500 lines of infrastructure code. 5. **No settlement data**: Scrapers can tell you what the odds ARE, but not whether a bet WON. You need a separate data source for results. ## The API Approach The same data, structured and reliable: ```python theme={null} # odds_api.py — Get odds via FieldFunded API import requests API_KEY = "your_api_key_here" BASE = "https://api.fieldfunded.com/v1" H = {"X-API-Key": API_KEY} # Get all Premier League events events = requests.get( f"{BASE}/events", headers=H, params={"sport": "soccer", "league": "england_epl"} ).json() for event in events["events"]: # Get full odds (all markets) odds = requests.get( f"{BASE}/events/{event['id']}/odds", headers=H ).json() print(f"\n{event['home_team']} vs {event['away_team']}") print(f" {len(odds['markets'])} markets available") # Show match winner for market in odds["markets"]: if "1x2" in market["name"].lower(): for sel in market["selections"]: print(f" {sel['name']}: {sel['odds']}") ``` ### What You Get * 30+ sports (soccer, basketball, tennis, NFL, NBA, esports) * 1,000+ markets per event (match winner, over/under, player props, corners, cards) * Live scores, fixtures, and team logos included * Automatic bet settlement — send a bet, get won/lost/refund back * No proxies, no CAPTCHAs, no selector maintenance ## Cost Comparison | Approach | Monthly cost | Your time | | ----------------------- | ------------ | ------------------------------- | | Scraping (no proxies) | \$0 | 4-8 hours/month fixing breakage | | Scraping (with proxies) | \$20-50 | 2-4 hours/month | | FieldFunded Free tier | \$0 | 0 hours | | FieldFunded Starter | \$29 | 0 hours | The free tier includes 10,000 requests/month — enough for most personal projects, bots, and prototypes. See the [full pricing comparison](/compare/pricing). ## When Scraping Still Makes Sense Be honest — scraping is the right choice in some cases: * **One-off research**: You need odds data once for an analysis, and you do not need reliability * **Niche sites**: The data you need is not available in any API (e.g., prop betting on obscure regional leagues) * **Learning**: You want to practice web scraping as a skill For everything else — bots, apps, dashboards, production systems — use an API. ## Hybrid Approach Some developers use both: an API for core data and scraping for supplementary data: ```python theme={null} import requests API_KEY = "your_api_key_here" BASE = "https://api.fieldfunded.com/v1" H = {"X-API-Key": API_KEY} # Primary: structured API data odds = requests.get( f"{BASE}/events/{event_id}/odds", headers=H ).json() # Use API odds as ground truth match_winner = None for m in odds["markets"]: if "winner" in m["name"].lower(): match_winner = {s["name"]: s["odds"] for s in m["selections"]} print("Match Winner Odds:", match_winner) ``` ## Migration Checklist (Scraper → API) If you currently have a scraper and want to switch: * [ ] Sign up for a [free API key](/quickstart) * [ ] Map your scraped fields to API response fields * [ ] Replace `BeautifulSoup` selectors with API calls * [ ] Remove proxy infrastructure * [ ] Add settlement checks (new capability you did not have before) * [ ] Delete your CAPTCHA-solving code *** ## Related Guides * [Get Live Odds with Python](/blog/get-live-odds-python) — complete Python tutorial * [Build a Settlement Engine](/guides/build-settlement-engine) — automate bet resolution * [Free Sports API Comparison](/compare/free-sports-api) — compare free tier options * [Build a Line Tracker](/guides/build-line-tracker) — track odds movements over time Replace your scraper in 5 minutes — 10,000 free requests/month All plans compared side by side